September 10, 2025Deleted JournalOpen Access

M3: multimodal artificial intelligence for medical report generation and visual question answering from 3D abdominal CT scans

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The optimized model achieved an average GREEN score of 0.409 for generating medical reports and 79% accuracy for answering visual questions.
These advancements in multimodal AI may lead to enhanced efficiencies in utilizing 3D CT scans for clinical decision-making.
The study focuses on the underexplored potential of 3D imaging in clinical workflows, emphasizing the need for innovative AI approaches.
Findings suggest that integrating visual and textual data enhances the analysis of medical imaging, which is vital for patient care.

Authors

AHAbdullah HosseiniWeill Cornell Medical College in Qatar AAAhmed I. Abd‐ElhamidSlovak Academy of Sciences ASAhmed SeragWeill Cornell Medical College in Qatar

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Implication

Observational analysis improved medical report generation and visual question answering, indicating multimodal AI's effectiveness in abdominal imaging.

Cite This Study

Hosseini et al. (2025) studied this question.

synapsesocial.com/papers/68c23fc3b210217d64797f7a https://doi.org/10.1093/bjrai/ubaf011

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Implication

Observational analysis improved medical report generation and visual question answering, indicating multimodal AI's effectiveness in abdominal imaging.

M3: multimodal artificial intelligence for medical report generation and visual question answering from 3D abdominal CT scans

Key Points

Discussion

Implication

Cite This Study

M3: multimodal artificial intelligence for medical report generation and visual question answering from 3D abdominal CT scans

Key Points

Discussion

Implication

Cite This Study